Holur Pavan, Shahsavari Shadi, Ebrahimzadeh Ehsan, Tangherlini Timothy R, Roychowdhury Vwani
Department of Electrical and Computer Engineering, University of California Los Angeles, Los Angeles, CA, USA.
Department of Scandinavian, University of California Berkeley, Berkeley, CA, USA.
R Soc Open Sci. 2021 Dec 22;8(12):210797. doi: 10.1098/rsos.210797. eCollection 2021 Dec.
Social reading sites offer an opportunity to capture a segment of readers' responses to literature, while data-driven analysis of these responses can provide new critical insight into how people 'read'. Posts discussing an individual book on the social reading site, , are referred to as 'reviews', and consist of summaries, opinions, quotes or some mixture of these. Computationally modelling these reviews allows one to discover the non-professional discussion space about a work, including an aggregated summary of the work's plot, an implicit sequencing of various subplots and readers' impressions of main characters. We develop a pipeline of interlocking computational tools to extract a representation of this reader-generated shared narrative model. Using a corpus of reviews of five popular novels, we discover readers' distillation of the novels' main storylines and their sequencing, as well as the readers' varying impressions of characters in the novel. In so doing, we make three important contributions to the study of infinite-vocabulary networks: (i) an automatically derived narrative network that includes meta-actants; (ii) a sequencing algorithm, REV2SEQ, that generates a consensus sequence of events based on partial trajectories aggregated from reviews, and (iii) an 'impressions' algorithm, SENT2IMP, that provides multi-modal insight into readers' opinions of characters.
社交阅读网站提供了一个机会来捕捉读者对文学作品的部分反应，而对这些反应进行数据驱动的分析可以为人们如何“阅读”提供新的批判性见解。在社交阅读网站上讨论某一本具体书籍的帖子被称为“书评”，它们包含内容摘要、观点、引文或这些内容的某种组合。对这些书评进行计算建模，可以让人们发现关于一部作品的非专业讨论空间，包括对作品情节的汇总概述、各个子情节的隐含顺序以及读者对主要人物的印象。我们开发了一套相互关联的计算工具流程，以提取这种读者生成的共享叙事模型的一种表示形式。使用五部畅销小说的书评语料库，我们发现了读者对小说主要故事情节的提炼及其顺序，以及读者对小说中人物的不同印象。在此过程中，我们对无限词汇网络的研究做出了三项重要贡献：（i）一个自动推导的包含元行动者的叙事网络；（ii）一种排序算法REV2SEQ，它基于从书评中汇总的部分轨迹生成事件的共识序列；（iii）一种“印象”算法SENT2IMP，它能为读者对人物的看法提供多模态见解。
